code: def test_envpool(self):
      bs = 32
      envs = moolib.EnvPool(
          Env, batch_size=bs, num_batches=2, num_processes=4
      )
  
      with pytest.raises(RuntimeError):
          envs.step(0, torch.zeros(bs))
      with pytest.raises(RuntimeError):
          envs.step(0, torch.zeros(bs + 1).long())
  
      z = torch.zeros(bs).long()
  
      initial = torch.ones(4, 4)
      initial[0][0] = 4.0
      initial[3][1] = 0.5
      initial[1][2] = 0.25
      initial = initial.expand(bs, 4, 4)
  
      obs = envs.step(batch_index=0, action=z).result()
      assert obs["n"].equal(initial)
      fut0 = envs.step(batch_index=0, action=z + 1)
      fut1 = envs.step(batch_index=1, action=z)
      obs = fut0.result()
      assert obs["n"].equal(initial * 2)
      obs = fut1.result()
      assert obs["n"].equal(initial)
      obs = envs.step(batch_index=0, action=z + 2).result()
      assert obs["n"].equal(initial)
  
      states = [initial.clone(), initial.clone()]
  
      for _ in range(100):
          index = random.randint(0, 1)
          action = torch.randint(0, 3, [bs])
          s = states[index]
          obs = envs.step(index, action).result()
          reward = obs["reward"]
          done = obs["done"]
          for i in range(bs):
              if action[i] == 1:
                  s[i] *= 2
              elif action[i] == 2:
                  s[i] /= 2
              r = abs(s[i].sum() - 4)
              d = s[i].sum() < 1
              if d:
                  s[i] = initial[i]
              assert d == done[i]
              assert s[i].equal(obs["n"][i])
              assert r == reward[i]
  
file_path: moolib/test/unit/test_envpool.py
imports: import random
  import pytest
  import torch
  import moolib
label_desc: Use image node_id 1 for calling the TestMoolibpEnvPool obj's underlying member method code with example usage: obj.test_envpool() without return types